Quality Prediction of Open Educational Resources A Metadata-based Approach

Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören

doi:10.1109/icalt49669.2020.00007

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…Therefore, it is clear that there is a significant need for automatic and intelligent metadata quality assessment in order to improve the discoverability, usability, and reusability of OERs [5]. Subsequently, a recently brief preliminary analysis was conducted on the current state of OER metadata in order to establish a quality prediction model [19]. As a result, we conclude that: (1) it is worthwhile and timely to consider analyzing OER metadata to improve OER-based services; and (2) there is a lack of intelligent prediction model that evaluates the quality of OERs based on their metadata to facilitate the quality control.…”

Section: Lessons Learnedmentioning

confidence: 99%

“…As a result, we conclude that: (1) it is worthwhile and timely to consider analyzing OER metadata to improve OER-based services; and (2) there is a lack of intelligent prediction model that evaluates the quality of OERs based on their metadata to facilitate the quality control. For the above mentioned reasons, in this paper, we attempt to follow-up, extend and evaluate the OER metadata quality prediction model suggested by [19], by using a video content based OER dataset, consisting of educational videos from Youtube. This was done in order to show the scalability and the generalizability of the proposed approach.…”

Section: Lessons Learnedmentioning

confidence: 99%

“…However, the lack of high-quality services, such as OER search and recommendation systems, limit the use of OERs [3,20,22]. In order to provide such services, high-quality metadata that describe OERs thoroughly and reliably are essential [19]. Although most of the OER repositories are using standardized metadata definitions (e.g., IEEE Standard for Learning Object Metadata (LOM) [6] and Learning Resource Metadata Initiative (LRMI) [1]) to improve open educational services, the lack or low-quality of metadata still limits the performance of these initiatives [7,11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metadata Analysis of Open Educational Resources

Tavakoli¹,

Elias²,

Kismihók³

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.

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Section: Lessons Learnedmentioning

confidence: 99%

Section: Lessons Learnedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metadata Analysis of Open Educational Resources

Tavakoli¹,

Elias²,

Kismihók³

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, ERs and OERs usually come with low-quality metadata [33], and they are isolated from other, content-wise similar ERs. That is one of the crucial reasons for lacking high-quality services, such as recommendation and search services, based on OERs [32]. Therefore, it is not surprising that the Semantic Web (SW) community shows increased interest in organising and classifying ERs, and enhancing the metadata in publicly available ER and OER [13,25].…”

Section: Introductionmentioning

confidence: 99%

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou

Abu-Rasheed

Tavakoli

et al. 2021

Lecture Notes in Computer Science

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With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.Resource: https://tibonto.github.io/educor/.

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“…That is one of the important reasons for lacking high-quality services (e.g. recommendation and search services) based on OERs [32]. Therefore, it is not surprising that the Semantic Web (SW) community shows increased interest in organising and classifying ERs, and enhancing the metadata in publicly available ER and OER [13,25].…”

Section: Introductionmentioning

confidence: 99%

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou,

Abu-Rasheed,

Tavakoli

et al. 2021

Preprint

Self Cite

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With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multisource learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user's learning goals, academic and psychological parameters, and the labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the reallife learning platform eDoer is discussed as a use-case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.

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Quality Prediction of Open Educational Resources A Metadata-based Approach

Cited by 13 publications

References 11 publications

Metadata Analysis of Open Educational Resources

Metadata Analysis of Open Educational Resources

EduCOR: An Educational and Career-Oriented Recommendation Ontology

EduCOR: An Educational and Career-Oriented Recommendation Ontology

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